The Use of Conductive Lycra Fabric in the Prototype Design of a Wearable Device to Monitor Physiological Signals
Bibliographic record
Abstract
Wearable technology has become commonplace for the measurement of heart rate, steps taken, and monitoring exercise regimes. However, wearables can also be used to enable or enhance the lives of persons living with disabilities. This paper discusses the design of a wearable device that aims to facilitate the assessment of physiological signals using conductive Lycra fabric. The device will be applicable for daily use within diverse contexts including the evaluation of emotional experiences of children with Severe Motor and Communication Impairment and the detection of Obstructive Sleep Apnea in children with Down Syndrome. The Lycra fabric sensors are used to acquire electrocardiographic signals, galvanic skin response, and respiratory signals. Articulated design requirements include constraints related to the ability to fit children of all sizes, and meeting medical device standards and biocompatibility, and criteria related to low costs, comfortability, and maintainability. Upon prototyping and preliminary testing, this device was found to offer an affordable, comfortable, and accessible solution to the monitoring of physiological signals. Clinical Relevance- This research provides initial knowledge and momentum towards an affordable wearable device using conductive Lycra to effectively monitor and assess physiological signals in children with disabilities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".